A Critical Comparison of Time Domain Load Identification Methods

Nordström, Lars; Nordberg, T. Patrik

doi:10.1299/jsmeintmovic.6.2.1151

Cited by 26 publications

(10 citation statements)

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“…Force identification problems were initially solved off-line in a deterministic setting. Many methods were proposed, most of them based on the inversion of the frequency response function [1,2,3] or making use of a time domain approach [4,5,6,7]. Several state estimation algorithms have been proposed for linear as well as for non-linear systems [8,9,10,11].…”

Section: Introductionmentioning

confidence: 99%

Design of sensor networks for instantaneous inversion of modally reduced order models in structural dynamics

Maes

Lourens

Nimmen

et al. 2015

Mechanical Systems and Signal Processing

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In structural dynamics, the forces acting on a structure are often not well known. System inversion techniques may be used to estimate these forces from the measured response of the structure. This paper first derives conditions for the invertibility of linear system models that apply to any instantaneous input estimation or joint input-state estimation algorithm. The conditions ensure the identifiability of the dynamic forces and system states, their stability and uniqueness. The present paper considers the specific case of modally reduced order models, which are generally obtained from a physical, finite element model, or from experimental data. It is shown how in this case the conditions can be directly expressed in terms of the modal properties of the structure. A distinction is made between input estimation and joint input-state estimation. Each of the conditions is illustrated by a conceptual example. The practical implementation is discussed for a case study where a sensor network for a footbridge is designed.

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Section: Introductionmentioning

confidence: 99%

Design of sensor networks for instantaneous inversion of modally reduced order models in structural dynamics

Maes

Lourens

Nimmen

et al. 2015

Mechanical Systems and Signal Processing

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“…(7)- (9) it is implied that the state variables are discretized using N s nodes in time whereas the load is discretized using N p time-nodes. We point out that the time partitioning related to ϕ n and φ m are formally independent and do not need to be uniform.…”

Section: Finite Element Formulationmentioning

confidence: 99%

“…Force or load identification problems have been studied extensively in literatures, e.g. [2,6,9,13,15,17].…”

mentioning

confidence: 99%

Load identification for a rolling disc: finite element discretization and virtual calibration

2011

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For many applications, direct measurement of forces in mechanical systems is difficult or even impossible, and indirect measurement involving inverse analysis must be adopted. One such application of major industrial relevance is the accurate measurement of contact forces acting on rolling bodies. In this paper, a newly proposed strategy for load identification is applied to an example problem of a rolling disc. Based on strain gauge measurements, the contact force is estimated using finite element analysis. A virtual calibration procedure is introduced in order to reduce the dependency of the results on the spatial discretization. In particular, the sensitivity of the results with respect to finite element discretization, sensor placement and noise is discussed. Numerical results based on synthetic data illustrate the behavior and accuracy of the proposed strategy.

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“…Many force identification algorithms have been proposed in the literature [1,2,3,4,5,6,7]. These methods can be distinguished by the way they tackle the ill-posedness of the force identification problem [8].…”

Section: Introductionmentioning

confidence: 99%

Verification of joint input-state estimation for force identification by means of in situ measurements on a footbridge

Maes

Nimmen

Lourens

et al. 2016

Mechanical Systems and Signal Processing

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This paper presents a verification of a joint input-state estimation algorithm using data obtained from in situ experiments on a footbridge. The estimation of the input and the system states is performed in a minimum-variance unbiased way, based on a limited number of response measurements and a system model. A dynamic model of the footbridge is obtained using a detailed finite element model that is updated using a set of experimental modal characteristics. The joint inputstate estimation algorithm is used for the identification of two impact, harmonic, and swept sine forces applied to the bridge deck. In addition to these forces, unknown stochastic forces, such as wind loads, are acting on the structure. These forces, as well as measurement errors, give rise to uncertainty in the estimated forces and system states. Quantification of the uncertainty requires determination of the power spectral density of the unknown stochastic excitation, which is identified from the structural response under ambient loading. The verification involves comparing the estimated forces with the actual, measured forces. Although a good overall agreement is obtained between the estimated and measured forces, modeling errors prohibit a proper distinction between multiple forces applied to the structure for the case of harmonic and swept sine excitation.

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A Critical Comparison of Time Domain Load Identification Methods

Cited by 26 publications

References 0 publications

Design of sensor networks for instantaneous inversion of modally reduced order models in structural dynamics

Design of sensor networks for instantaneous inversion of modally reduced order models in structural dynamics

Load identification for a rolling disc: finite element discretization and virtual calibration

Verification of joint input-state estimation for force identification by means of in situ measurements on a footbridge

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